Hierarchic Analysis Method to Evaluate Rock Burst Risk

Ji, Ming; Guo, Hongjun; Zhang, Yidong; Li, Tao; Gao, Linsheng

doi:10.1155/2015/184398

Cited by 4 publications

(4 citation statements)

References 5 publications

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“…Based on the literature investigation and on-the-spot measurement of tunnel, the following indexes are selected to describe a tunnel section [27][28][29][30][31][32][33]: the Russian criterion value C 1 (C 1 � σ θmax /σ C , i.e., the ratio of maximum tangential stress of chamber σ θmax to rock uniaxial compressive strength σ C ), rock brittleness coefficient C 2 (C 2 � σ C /σ t , i.e., ratio of uniaxial compressive strength σ C to uniaxial tensile strength of rock σ t ), the grade of groundwater condition C 3 and rock integrity coefficient C 4 (Kv value). e above indexes reflect the different attributes of the tunnel section, respectively.…”

Section: Application In Rockburst Predictionmentioning

confidence: 99%

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Zheng

Zhong

Fang

et al. 2019

Advances in Civil Engineering

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A rockburst prediction model of the entropy weight grey relational backpropagation (BP) neural network is developed. The model needs to select the evaluation factors according to the engineering practice and establish the sample library. The entropy weight method is used to calculate the objective weight of the characteristic factors, and the similarity between the samples is calculated by the combination of grey relational theory and the entropy method. The training sample of the BP neural network is selected by threshold determination. Finally, we use the trained neural network to estimate the rockburst intensity grade of samples to be tested. This model is applied to the rockburst prediction of Qamchiq tunnel project, and the prediction results are in good agreement with the actual conditions of the subsequent construction, thus verifying the feasibility and effectiveness of the model in the rockburst prediction.

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Section: Application In Rockburst Predictionmentioning

confidence: 99%

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Zheng

Zhong

Fang

et al. 2019

Advances in Civil Engineering

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“…Manifesting stability and destruction level of gearbox's degradation and malfunction [36] Kurtosis index…”

Section: Damage Indicators Feature Symbol Equation Implicationmentioning

confidence: 99%

Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox

Zhou

Xiao

et al. 2019

Entropy

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The prediction of electrical machines’ Remaining Useful Life (RUL) can facilitate making electrical machine maintenance policies, which is important for improving their security and extending their life span. This paper proposes an RUL prediction model with similarity fusion of multi-parameter and multi-sample. Firstly, based on the time domain and frequency domain extraction of vibration signals, the performance damage indicator system of a gearbox is established to select the optimal damage indicators for RUL prediction. Low-pass filtering based on approximate entropy variance (Aev) is introduced in this process because of its stability. Secondly, this paper constructs Dynamic Time Warping Distance (DTWD) as a similarity measurement function, which belongs to the nonlinear dynamic programming algorithm. It performed better than the traditional Euclidean distance. Thirdly, based on DTWD, similarity fusion of multi-parameter and multi-sample methods is proposed here to achieve RUL prediction. Next, the performance evaluation indicator Q is adopted to evaluate the RUL prediction accuracy of different methods. Finally, the proposed method is verified by experiments, and the Multivariable Support Vector Machine (MSVM) and Principal Component Analysis (PCA) are introduced for comparative studies. The results show that the Mean Absolute Percentage Error (MAPE) of the similarity fusion of multi-parameter and multi-sample methods proposed here is below 14%, which is lower than MSVM’s and PCA’s. Additionally, the RUL prediction based on the DTWD function in multi-sample similarity fusion exhibits the best accuracy.

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“…Therefore, inaccurate early warning cases would occur. Moreover, the uncertainty theories mainly include fuzzy mathematics [18,19], the rough set theory [20,21], the cloud model theory [22,23], the analytic hierarchy process (AHP) [24,25], the grey system theory [26], and the set pair analysis [27], which have been broadly applied to the prediction study of rock bursts. Although these methods introduce fuzzy and random characteristics to the influencing factors of rock bursts [28], the evaluation and analysis of the rock burst indicators mainly rely on experience and are relatively subjective, leading to a large gap between the prediction results and the actual situation.…”

Section: Introductionmentioning

confidence: 99%

Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model

Yuan,

Ji,

Liu

et al. 2023

Applied Sciences

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In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshitai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance.

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Hierarchic Analysis Method to Evaluate Rock Burst Risk

Cited by 4 publications

References 5 publications

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Rockburst Prediction Model Based on Entropy Weight Integrated with Grey Relational BP Neural Network

Remaining Useful Life Prediction with Similarity Fusion of Multi-Parameter and Multi-Sample Based on the Vibration Signals of Diesel Generator Gearbox

Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model

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